[pkgsrc/trunk]: pkgsrc/math/py-statsmodels Update py-statsmodels to 0.12.2

[prlw1 <prlw1%pkgsrc.org@localhost>]

details:   https://anonhg.NetBSD.org/pkgsrc/rev/828a7097a05c
branches:  trunk
changeset: 449820:828a7097a05c
user:      prlw1 <prlw1%pkgsrc.org@localhost>
date:      Tue Apr 06 12:16:47 2021 +0000

description:
Update py-statsmodels to 0.12.2

Many many changes including

Oneway ANOVA-type analysis
~~~~~~~~~~~~~~~~~~~~~~~~~~

Several statistical methods for ANOVA-type analysis of k independent samples
have been added in module :mod:`~statsmodels.stats.oneway`. This includes
standard Anova, Anova for unequal variances (Welch, Brown-Forsythe for mean),
Anova based on trimmed samples (Yuen anova) and equivalence testing using
the method of Wellek.
Anova for equality of variances or dispersion are available for several
transformations. This includes Levene test and Browne-Forsythe test for equal
variances as special cases. It uses the `anova_oneway` function, so unequal
variance and trimming options are also available for tests on variances.
Several functions for effect size measures have been added, that can be used
for reporting or for power and sample size computation.

Multivariate statistics
~~~~~~~~~~~~~~~~~~~~~~~

The new module :mod:`~statsmodels.stats.multivariate` includes one and
two sample tests for multivariate means, Hotelling's t-tests',
:func:`~statsmodels.stats.multivariate.test_mvmean`,
:func:`~statsmodels.stats.multivariate.test_mvmean_2indep` and confidence
intervals for one-sample multivariate mean
:func:`~statsmodels.stats.multivariate.confint_mvmean`
Additionally, hypothesis tests for covariance patterns, and for oneway equality
of covariances are now available in several ``test_cov`` functions.

New exponential smoothing model: ETS (Error, Trend, Seasonal)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

- Class implementing ETS models :class:`~statsmodels.tsa.exponential_smoothing.ets.ETSModel`.
- Includes linear and non-linear exponential smoothing models
- Supports parameter fitting, in-sample prediction and out-of-sample
  forecasting, prediction intervals, simulation, and more.
- Based on the innovations state space approach.

Forecasting Methods
~~~~~~~~~~~~~~~~~~~

Two popular methods for forecasting time series, forecasting after
STL decomposition (:class:`~statsmodels.tsa.forecasting.stl.STLForecast`)
and the Theta model
(:class:`~statsmodels.tsa.forecasting.theta.ThetaModel`) have been added.


See 0.12.0-0.12.2 at https://www.statsmodels.org/stable/release/
for the full story, including deprecations.

diffstat:

 math/py-statsmodels/Makefile |   23 ++--
 math/py-statsmodels/PLIST    |  200 ++++++++++++++++++++++++++++++++++++++++--
 math/py-statsmodels/distinfo |   10 +-
 3 files changed, 206 insertions(+), 27 deletions(-)

diffs (truncated from 476 to 300 lines):

diff -r 1f8888322b70 -r 828a7097a05c math/py-statsmodels/Makefile
--- a/math/py-statsmodels/Makefile      Tue Apr 06 12:16:08 2021 +0000
+++ b/math/py-statsmodels/Makefile      Tue Apr 06 12:16:47 2021 +0000
@@ -1,8 +1,7 @@
-# $NetBSD: Makefile,v 1.8 2020/10/12 21:52:04 bacon Exp $
+# $NetBSD: Makefile,v 1.9 2021/04/06 12:16:47 prlw1 Exp $
 
-DISTNAME=      statsmodels-0.11.1
+DISTNAME=      statsmodels-0.12.2
 PKGNAME=       ${PYPKGPREFIX}-${DISTNAME}
-PKGREVISION=   1
 CATEGORIES=    math python
 MASTER_SITES=  ${MASTER_SITE_PYPI:=s/statsmodels/}
 
@@ -11,15 +10,19 @@
 COMMENT=       Statistical computations and models for Python
 LICENSE=       modified-bsd
 
-BUILD_DEPENDS+=        ${PYPKGPREFIX}-cython>=0.24:../../devel/py-cython
-DEPENDS+=      ${PYPKGPREFIX}-pandas>=0.19:../../math/py-pandas
-DEPENDS+=      ${PYPKGPREFIX}-patsy>=0.4.0:../../math/py-patsy
-DEPENDS+=      ${PYPKGPREFIX}-scipy>=0.18:../../math/py-scipy
-
-PYTHON_VERSIONS_INCOMPATIBLE=  27      # py-matplotlib, py-scipy
+PYTHON_VERSIONS_INCOMPATIBLE=  36 27   # py-scipy
 USE_LANGUAGES=                 c
 
+BUILD_DEPENDS+=        ${PYPKGPREFIX}-cython>=0.29:../../devel/py-cython
+DEPENDS+=      ${PYPKGPREFIX}-pandas>=0.21:../../math/py-pandas
+DEPENDS+=      ${PYPKGPREFIX}-patsy>=0.5:../../math/py-patsy
+DEPENDS+=      ${PYPKGPREFIX}-scipy>=1.1:../../math/py-scipy
+
+post-extract:
+       ${CHMOD} -R o-w,g-w ${WRKSRC}
+       ${FIND} ${WRKSRC} -type f -printx | ${XARGS} ${CHMOD} a-x
+
 .include "../../lang/python/egg.mk"
-BUILDLINK_API_DEPENDS.py-numpy+=       ${PYPKGPREFIX}-numpy>=1.11
+BUILDLINK_API_DEPENDS.py-numpy+=       ${PYPKGPREFIX}-numpy>=1.15
 .include "../../math/py-numpy/buildlink3.mk"
 .include "../../mk/bsd.pkg.mk"
diff -r 1f8888322b70 -r 828a7097a05c math/py-statsmodels/PLIST
--- a/math/py-statsmodels/PLIST Tue Apr 06 12:16:08 2021 +0000
+++ b/math/py-statsmodels/PLIST Tue Apr 06 12:16:47 2021 +0000
@@ -1,4 +1,4 @@
-@comment $NetBSD: PLIST,v 1.6 2020/05/03 16:13:11 minskim Exp $
+@comment $NetBSD: PLIST,v 1.7 2021/04/06 12:16:47 prlw1 Exp $
 ${PYSITELIB}/${EGG_INFODIR}/PKG-INFO
 ${PYSITELIB}/${EGG_INFODIR}/SOURCES.txt
 ${PYSITELIB}/${EGG_INFODIR}/dependency_links.txt
@@ -1209,12 +1209,10 @@
 ${PYSITELIB}/statsmodels/regression/tests/test_tools.py
 ${PYSITELIB}/statsmodels/regression/tests/test_tools.pyc
 ${PYSITELIB}/statsmodels/regression/tests/test_tools.pyo
-${PYSITELIB}/statsmodels/resampling/__init__.py
-${PYSITELIB}/statsmodels/resampling/__init__.pyc
-${PYSITELIB}/statsmodels/resampling/__init__.pyo
 ${PYSITELIB}/statsmodels/robust/__init__.py
 ${PYSITELIB}/statsmodels/robust/__init__.pyc
 ${PYSITELIB}/statsmodels/robust/__init__.pyo
+${PYSITELIB}/statsmodels/robust/_qn.so
 ${PYSITELIB}/statsmodels/robust/norms.py
 ${PYSITELIB}/statsmodels/robust/norms.pyc
 ${PYSITELIB}/statsmodels/robust/norms.pyo
@@ -1710,6 +1708,9 @@
 ${PYSITELIB}/statsmodels/stats/mediation.py
 ${PYSITELIB}/statsmodels/stats/mediation.pyc
 ${PYSITELIB}/statsmodels/stats/mediation.pyo
+${PYSITELIB}/statsmodels/stats/meta_analysis.py
+${PYSITELIB}/statsmodels/stats/meta_analysis.pyc
+${PYSITELIB}/statsmodels/stats/meta_analysis.pyo
 ${PYSITELIB}/statsmodels/stats/moment_helpers.py
 ${PYSITELIB}/statsmodels/stats/moment_helpers.pyc
 ${PYSITELIB}/statsmodels/stats/moment_helpers.pyo
@@ -1719,12 +1720,18 @@
 ${PYSITELIB}/statsmodels/stats/multitest.py
 ${PYSITELIB}/statsmodels/stats/multitest.pyc
 ${PYSITELIB}/statsmodels/stats/multitest.pyo
+${PYSITELIB}/statsmodels/stats/multivariate.py
+${PYSITELIB}/statsmodels/stats/multivariate.pyc
+${PYSITELIB}/statsmodels/stats/multivariate.pyo
 ${PYSITELIB}/statsmodels/stats/multivariate_tools.py
 ${PYSITELIB}/statsmodels/stats/multivariate_tools.pyc
 ${PYSITELIB}/statsmodels/stats/multivariate_tools.pyo
 ${PYSITELIB}/statsmodels/stats/oaxaca.py
 ${PYSITELIB}/statsmodels/stats/oaxaca.pyc
 ${PYSITELIB}/statsmodels/stats/oaxaca.pyo
+${PYSITELIB}/statsmodels/stats/oneway.py
+${PYSITELIB}/statsmodels/stats/oneway.pyc
+${PYSITELIB}/statsmodels/stats/oneway.pyo
 ${PYSITELIB}/statsmodels/stats/outliers_influence.py
 ${PYSITELIB}/statsmodels/stats/outliers_influence.pyc
 ${PYSITELIB}/statsmodels/stats/outliers_influence.pyo
@@ -1734,9 +1741,15 @@
 ${PYSITELIB}/statsmodels/stats/proportion.py
 ${PYSITELIB}/statsmodels/stats/proportion.pyc
 ${PYSITELIB}/statsmodels/stats/proportion.pyo
+${PYSITELIB}/statsmodels/stats/rates.py
+${PYSITELIB}/statsmodels/stats/rates.pyc
+${PYSITELIB}/statsmodels/stats/rates.pyo
 ${PYSITELIB}/statsmodels/stats/regularized_covariance.py
 ${PYSITELIB}/statsmodels/stats/regularized_covariance.pyc
 ${PYSITELIB}/statsmodels/stats/regularized_covariance.pyo
+${PYSITELIB}/statsmodels/stats/robust_compare.py
+${PYSITELIB}/statsmodels/stats/robust_compare.pyc
+${PYSITELIB}/statsmodels/stats/robust_compare.pyo
 ${PYSITELIB}/statsmodels/stats/sandwich_covariance.py
 ${PYSITELIB}/statsmodels/stats/sandwich_covariance.pyc
 ${PYSITELIB}/statsmodels/stats/sandwich_covariance.pyo
@@ -1764,6 +1777,9 @@
 ${PYSITELIB}/statsmodels/stats/tests/results/lilliefors_critical_value_simulation.pyc
 ${PYSITELIB}/statsmodels/stats/tests/results/lilliefors_critical_value_simulation.pyo
 ${PYSITELIB}/statsmodels/stats/tests/results/results_influence_logit.csv
+${PYSITELIB}/statsmodels/stats/tests/results/results_meta.py
+${PYSITELIB}/statsmodels/stats/tests/results/results_meta.pyc
+${PYSITELIB}/statsmodels/stats/tests/results/results_meta.pyo
 ${PYSITELIB}/statsmodels/stats/tests/results/results_multinomial_proportions.py
 ${PYSITELIB}/statsmodels/stats/tests/results/results_multinomial_proportions.pyc
 ${PYSITELIB}/statsmodels/stats/tests/results/results_multinomial_proportions.pyo
@@ -1776,6 +1792,9 @@
 ${PYSITELIB}/statsmodels/stats/tests/results/results_proportion.py
 ${PYSITELIB}/statsmodels/stats/tests/results/results_proportion.pyc
 ${PYSITELIB}/statsmodels/stats/tests/results/results_proportion.pyo
+${PYSITELIB}/statsmodels/stats/tests/results/results_rates.py
+${PYSITELIB}/statsmodels/stats/tests/results/results_rates.pyc
+${PYSITELIB}/statsmodels/stats/tests/results/results_rates.pyo
 ${PYSITELIB}/statsmodels/stats/tests/results/wspec1.csv
 ${PYSITELIB}/statsmodels/stats/tests/results/wspec2.csv
 ${PYSITELIB}/statsmodels/stats/tests/results/wspec3.csv
@@ -1786,6 +1805,9 @@
 ${PYSITELIB}/statsmodels/stats/tests/test_anova_rm.py
 ${PYSITELIB}/statsmodels/stats/tests/test_anova_rm.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_anova_rm.pyo
+${PYSITELIB}/statsmodels/stats/tests/test_base.py
+${PYSITELIB}/statsmodels/stats/tests/test_base.pyc
+${PYSITELIB}/statsmodels/stats/tests/test_base.pyo
 ${PYSITELIB}/statsmodels/stats/tests/test_contingency_tables.py
 ${PYSITELIB}/statsmodels/stats/tests/test_contingency_tables.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_contingency_tables.pyo
@@ -1832,18 +1854,27 @@
 ${PYSITELIB}/statsmodels/stats/tests/test_mediation.py
 ${PYSITELIB}/statsmodels/stats/tests/test_mediation.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_mediation.pyo
+${PYSITELIB}/statsmodels/stats/tests/test_meta.py
+${PYSITELIB}/statsmodels/stats/tests/test_meta.pyc
+${PYSITELIB}/statsmodels/stats/tests/test_meta.pyo
 ${PYSITELIB}/statsmodels/stats/tests/test_moment_helpers.py
 ${PYSITELIB}/statsmodels/stats/tests/test_moment_helpers.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_moment_helpers.pyo
 ${PYSITELIB}/statsmodels/stats/tests/test_multi.py
 ${PYSITELIB}/statsmodels/stats/tests/test_multi.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_multi.pyo
+${PYSITELIB}/statsmodels/stats/tests/test_multivariate.py
+${PYSITELIB}/statsmodels/stats/tests/test_multivariate.pyc
+${PYSITELIB}/statsmodels/stats/tests/test_multivariate.pyo
 ${PYSITELIB}/statsmodels/stats/tests/test_nonparametric.py
 ${PYSITELIB}/statsmodels/stats/tests/test_nonparametric.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_nonparametric.pyo
 ${PYSITELIB}/statsmodels/stats/tests/test_oaxaca.py
 ${PYSITELIB}/statsmodels/stats/tests/test_oaxaca.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_oaxaca.pyo
+${PYSITELIB}/statsmodels/stats/tests/test_oneway.py
+${PYSITELIB}/statsmodels/stats/tests/test_oneway.pyc
+${PYSITELIB}/statsmodels/stats/tests/test_oneway.pyo
 ${PYSITELIB}/statsmodels/stats/tests/test_outliers_influence.py
 ${PYSITELIB}/statsmodels/stats/tests/test_outliers_influence.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_outliers_influence.pyo
@@ -1862,9 +1893,15 @@
 ${PYSITELIB}/statsmodels/stats/tests/test_qsturng.py
 ${PYSITELIB}/statsmodels/stats/tests/test_qsturng.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_qsturng.pyo
+${PYSITELIB}/statsmodels/stats/tests/test_rates_poisson.py
+${PYSITELIB}/statsmodels/stats/tests/test_rates_poisson.pyc
+${PYSITELIB}/statsmodels/stats/tests/test_rates_poisson.pyo
 ${PYSITELIB}/statsmodels/stats/tests/test_regularized_covariance.py
 ${PYSITELIB}/statsmodels/stats/tests/test_regularized_covariance.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_regularized_covariance.pyo
+${PYSITELIB}/statsmodels/stats/tests/test_robust_compare.py
+${PYSITELIB}/statsmodels/stats/tests/test_robust_compare.pyc
+${PYSITELIB}/statsmodels/stats/tests/test_robust_compare.pyo
 ${PYSITELIB}/statsmodels/stats/tests/test_sandwich.py
 ${PYSITELIB}/statsmodels/stats/tests/test_sandwich.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_sandwich.pyo
@@ -2015,7 +2052,6 @@
 ${PYSITELIB}/statsmodels/tsa/_bds.py
 ${PYSITELIB}/statsmodels/tsa/_bds.pyc
 ${PYSITELIB}/statsmodels/tsa/_bds.pyo
-${PYSITELIB}/statsmodels/tsa/_exponential_smoothers.so
 ${PYSITELIB}/statsmodels/tsa/_innovations.so
 ${PYSITELIB}/statsmodels/tsa/_stl.so
 ${PYSITELIB}/statsmodels/tsa/adfvalues.py
@@ -2144,6 +2180,9 @@
 ${PYSITELIB}/statsmodels/tsa/base/datetools.py
 ${PYSITELIB}/statsmodels/tsa/base/datetools.pyc
 ${PYSITELIB}/statsmodels/tsa/base/datetools.pyo
+${PYSITELIB}/statsmodels/tsa/base/prediction.py
+${PYSITELIB}/statsmodels/tsa/base/prediction.pyc
+${PYSITELIB}/statsmodels/tsa/base/prediction.pyo
 ${PYSITELIB}/statsmodels/tsa/base/tests/__init__.py
 ${PYSITELIB}/statsmodels/tsa/base/tests/__init__.pyc
 ${PYSITELIB}/statsmodels/tsa/base/tests/__init__.pyo
@@ -2153,6 +2192,9 @@
 ${PYSITELIB}/statsmodels/tsa/base/tests/test_datetools.py
 ${PYSITELIB}/statsmodels/tsa/base/tests/test_datetools.pyc
 ${PYSITELIB}/statsmodels/tsa/base/tests/test_datetools.pyo
+${PYSITELIB}/statsmodels/tsa/base/tests/test_prediction.py
+${PYSITELIB}/statsmodels/tsa/base/tests/test_prediction.pyc
+${PYSITELIB}/statsmodels/tsa/base/tests/test_prediction.pyo
 ${PYSITELIB}/statsmodels/tsa/base/tests/test_tsa_indexes.py
 ${PYSITELIB}/statsmodels/tsa/base/tests/test_tsa_indexes.pyc
 ${PYSITELIB}/statsmodels/tsa/base/tests/test_tsa_indexes.pyo
@@ -2165,9 +2207,19 @@
 ${PYSITELIB}/statsmodels/tsa/descriptivestats.py
 ${PYSITELIB}/statsmodels/tsa/descriptivestats.pyc
 ${PYSITELIB}/statsmodels/tsa/descriptivestats.pyo
+${PYSITELIB}/statsmodels/tsa/deterministic.py
+${PYSITELIB}/statsmodels/tsa/deterministic.pyc
+${PYSITELIB}/statsmodels/tsa/deterministic.pyo
 ${PYSITELIB}/statsmodels/tsa/exponential_smoothing/__init__.py
 ${PYSITELIB}/statsmodels/tsa/exponential_smoothing/__init__.pyc
 ${PYSITELIB}/statsmodels/tsa/exponential_smoothing/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/exponential_smoothing/_ets_smooth.so
+${PYSITELIB}/statsmodels/tsa/exponential_smoothing/base.py
+${PYSITELIB}/statsmodels/tsa/exponential_smoothing/base.pyc
+${PYSITELIB}/statsmodels/tsa/exponential_smoothing/base.pyo
+${PYSITELIB}/statsmodels/tsa/exponential_smoothing/ets.py
+${PYSITELIB}/statsmodels/tsa/exponential_smoothing/ets.pyc
+${PYSITELIB}/statsmodels/tsa/exponential_smoothing/ets.pyo
 ${PYSITELIB}/statsmodels/tsa/exponential_smoothing/initialization.py
 ${PYSITELIB}/statsmodels/tsa/exponential_smoothing/initialization.pyc
 ${PYSITELIB}/statsmodels/tsa/exponential_smoothing/initialization.pyo
@@ -2204,9 +2256,47 @@
 ${PYSITELIB}/statsmodels/tsa/filters/tests/test_filters.py
 ${PYSITELIB}/statsmodels/tsa/filters/tests/test_filters.pyc
 ${PYSITELIB}/statsmodels/tsa/filters/tests/test_filters.pyo
-${PYSITELIB}/statsmodels/tsa/holtwinters.py
-${PYSITELIB}/statsmodels/tsa/holtwinters.pyc
-${PYSITELIB}/statsmodels/tsa/holtwinters.pyo
+${PYSITELIB}/statsmodels/tsa/forecasting/__init__.py
+${PYSITELIB}/statsmodels/tsa/forecasting/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/forecasting/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/forecasting/stl.py
+${PYSITELIB}/statsmodels/tsa/forecasting/stl.pyc
+${PYSITELIB}/statsmodels/tsa/forecasting/stl.pyo
+${PYSITELIB}/statsmodels/tsa/forecasting/tests/__init__.py
+${PYSITELIB}/statsmodels/tsa/forecasting/tests/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/forecasting/tests/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/forecasting/tests/test_stl.py
+${PYSITELIB}/statsmodels/tsa/forecasting/tests/test_stl.pyc
+${PYSITELIB}/statsmodels/tsa/forecasting/tests/test_stl.pyo
+${PYSITELIB}/statsmodels/tsa/forecasting/tests/test_theta.py
+${PYSITELIB}/statsmodels/tsa/forecasting/tests/test_theta.pyc
+${PYSITELIB}/statsmodels/tsa/forecasting/tests/test_theta.pyo
+${PYSITELIB}/statsmodels/tsa/forecasting/theta.py
+${PYSITELIB}/statsmodels/tsa/forecasting/theta.pyc
+${PYSITELIB}/statsmodels/tsa/forecasting/theta.pyo
+${PYSITELIB}/statsmodels/tsa/holtwinters/__init__.py
+${PYSITELIB}/statsmodels/tsa/holtwinters/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/holtwinters/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/holtwinters/_exponential_smoothers.so
+${PYSITELIB}/statsmodels/tsa/holtwinters/_smoothers.py
+${PYSITELIB}/statsmodels/tsa/holtwinters/_smoothers.pyc
+${PYSITELIB}/statsmodels/tsa/holtwinters/_smoothers.pyo
+${PYSITELIB}/statsmodels/tsa/holtwinters/model.py
+${PYSITELIB}/statsmodels/tsa/holtwinters/model.pyc
+${PYSITELIB}/statsmodels/tsa/holtwinters/model.pyo
+${PYSITELIB}/statsmodels/tsa/holtwinters/results.py
+${PYSITELIB}/statsmodels/tsa/holtwinters/results.pyc
+${PYSITELIB}/statsmodels/tsa/holtwinters/results.pyo
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/__init__.py
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/results/__init__.py
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/results/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/results/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/results/housing-data.csv
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/test_holtwinters.py
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/test_holtwinters.pyc
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/test_holtwinters.pyo
 ${PYSITELIB}/statsmodels/tsa/innovations/__init__.py
 ${PYSITELIB}/statsmodels/tsa/innovations/__init__.pyc
 ${PYSITELIB}/statsmodels/tsa/innovations/__init__.pyo
@@ -2286,6 +2376,7 @@
 ${PYSITELIB}/statsmodels/tsa/statespace/__init__.py
 ${PYSITELIB}/statsmodels/tsa/statespace/__init__.pyc
 ${PYSITELIB}/statsmodels/tsa/statespace/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/_cfa_simulation_smoother.so
 ${PYSITELIB}/statsmodels/tsa/statespace/_filters/__init__.py
 ${PYSITELIB}/statsmodels/tsa/statespace/_filters/__init__.pyc
 ${PYSITELIB}/statsmodels/tsa/statespace/_filters/__init__.pyo
@@ -2299,6 +2390,9 @@
 ${PYSITELIB}/statsmodels/tsa/statespace/_pykalman_smoother.py
 ${PYSITELIB}/statsmodels/tsa/statespace/_pykalman_smoother.pyc
 ${PYSITELIB}/statsmodels/tsa/statespace/_pykalman_smoother.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/_quarterly_ar1.py
+${PYSITELIB}/statsmodels/tsa/statespace/_quarterly_ar1.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/_quarterly_ar1.pyo
 ${PYSITELIB}/statsmodels/tsa/statespace/_representation.so
 ${PYSITELIB}/statsmodels/tsa/statespace/_simulation_smoother.so
 ${PYSITELIB}/statsmodels/tsa/statespace/_smoothers/__init__.py